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Generation of High Quality Chromatin Immunoprecipitation DNA Template for High-throughput Sequencing ChIP-seq
Published on: April 19, 2013
Exploring biomarkers and transcriptional factors in type 2 diabetes by comprehensive bioinformatics analysis on
Yalan Huang1, Linkun Cai2, Xiu Liu3
1College of Graduate, Hunan University of Traditional Chinese Medicine, Changsha, China.
Background:
Type 2 diabetes (T2D) is a prevalent chronic disease with elusive. Combining transcriptome and single-cell sequencing data to explore biomarkers of T2D could provide new insights into the in-depth understanding of the molecular mechanisms and diagnosis of T2D.
Methods:
The GSE41762 dataset including RNA-seq data for healthy and T2D patients, was obtained from the Gene Expression Omnibus (GEO) database. The potential functions of the differentially expressed genes (DEGs) were revealed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. Moreover, biomarkers were screened out by the Least Absolute Shrinkage and Selection Operator (LASSO) algorithm and receiver operating characteristic (ROC) analysis. Furthermore, single-cell RNA (sc-RNA)-seq data in the "E-MTAB-5061" dataset was downloaded from the ArrayExpress (European Bioinformatics Institute, EBI) database. Principal components analysis (PCA) and t-distributed stochastic neighbor embedding (tSNE) were used for dimensionality reduction analysis and cell clustering. The FindAllMarkers function was used annotate different cell clusters, and key cell clusters were screened by the expression levels of the biomarkers. Finally, the transcription factors (TFs) of the biomarkers were recognized.
Results:
A total of 111 DEGs were screened in the GSE41762 dataset, which were mainly related to hormone secretion, specialized postsynaptic membrane, pancreatic secretion, JAK-STAT signaling pathway, and Ras signaling pathway. In addition, SLC2A2, SERPINF1, RASGRP1, and CHL1 were screened out as biomarkers of T2D, which possessed potential diagnostic value as AUC value greater than 0.8. A total of 1,515 T2D group cells and 1,817 healthy cohort cells were screened as core cells in the "E-MTAB-5061" dataset. Following tSNE dimensionality reduction cluster analysis, the core cells were divided into 13 cell clusters. According to the marker genes, the 13 cell clusters were annotated into six types of cells. Notably, SERPINF1 was highly expressed in fibroblasts and might be regulated by NR2F2 (nuclear receptor subfamily2, group F, and member 2).
Conclusions:
This study identified four biomarkers (SLC2A2, SERPINF1, RASGRP1, and CHL1) for T2D, which provided new markers for the clinical diagnosis of T2D. Among them, SERPINF1 might be regulated by NR2F2, which provides valuable insight into the pathogensis of T2D.
